New Potential Cotton (Gossypium hirsutum L.) Varieties for Farmers in Rain-Fed Agro-Ecologies of North Western Ethiopia

Kedir, WH; Gudeta, N; Bedada, G

doi:10.15580/GJAS.2018.12.122118179

INTRODUCTION

Cotton, the king of fiber crops, is one of the momentous and an important cash crop exercising profound influence on economics and social affairs of the world. Any other fiber crop cannot compete with cotton for its fiber quality. It is also known as “White Gold”. Apart from world’s leading natural fiber, cotton is world’s second most important oilseed crop (Kohel, 1987). Primarily it is industrial raw material in textile manufacturing which provides employment to millions of people all over the world for various activities such as cultivation, seed production, marketing, industrial utilization and research.

Cotton is most widely cultivated crop in the world and has attained main focus of research of which upland cotton (Gossypium hirsutum L.) species meet 90% of the bulk world’s cotton demand. Upland cotton belongs to the genus Gossypium, which consists of 45 diploid and five Allotetraploid species which are distributed mostly in tropical and subtropical areas of the world. Historically, natural hybridization has played an important role in the evolution of modern cultivated cottons (Wendel and Crown, 2003).

The genetic variability of a trait within a population is the proportion of observable differences in a trait between individuals within a population that is due to genetic differences. Factors including genetics, environment and random chance can all contribute to the variation between individuals in their observable characteristics. Heritability measures the fraction of phenotype variability that can be attributed to genetic variation (Raj et al., 2008).

To develop high yielding varieties of cotton, genetic information regarding different quantitative and qualitative traits is helpful to cotton breeders to improve genetic architecture of the crop in a particular direction, to improve and attain the proper production level of the crop (Nadeem and Azhar et al., 2004; Ali & Khan, 2007 and Abbas et al., 2008). The use of existing genetic variability in the breeding material and the creation of new variability along with the underlining knowledge on the genetic behavior are of crucial importance for this purpose in a breeding program (Basal and Turgut, 2005; Abbas et al., 2008; Ali et al., 2008; and Ali and Awan, 2009).

To address the current demand and supply gap targeted, intervention works in developing
high yielding and excellent quality lint genotypes should be one of the main components to step forward the cotton sector through exploiting the available genetic resources. To
set proper breeding strategy and exploit genetic potential of existing genotypes, understanding of genetic variability is very crucial.

Yield is a complex polygenic character which is final product resulting from the interaction of yield attributing characters. For rational improvement of seed cotton yield, the understanding of relationship of component traits with yield is very essential to make effective selection and also simultaneous improvement of most characters. Keeping the above facts in view, the present study was, therefore, planned with the following specific objectives:

1. To estimate heritability of quantitative and qualitative traits of cotton genotypes.

2. To analyze the correlation of seed cotton yield with yield attributing components.

3. To assess and select high yielding potential varieties for rain-fed areas.

MATERIALS AND METHODS

Description of the study area

The experiment was conducted at Homosha district in Benishangul Gumuz Regional State in the western part of Ethiopia during the main cropping season of 2017/18.It is one of the potential area for cotton production in the Region.

Homosha is located 38 km north of Assosa town and 701 km west of Addis Ababa with an altitude of 1390 meter above sea level and found at 10⁰18.764' N latitude and 034⁰38.630' E longitude. This district has also a uni-modal rainfall pattern, which starts at the end of April and extends to mid of October. Its maximum rainfall is received during June to October (AsARC Report, 2013) while its major soil type is Nitosol with a dark reddish brown color (AsARC Report, 2011). And also, its optimum temperature range is 28 to 32^◦C.

Experimental Materials and Design

In this study, a total of 14 genotypes including 12 elite genotypes and two check varieties were evaluated at Homosha (Table 1). These genotypes were obtained from Werer Agricultural Research Center (WARC). The genotypes were organized in a randomized complete block design with three replications. Five rows of 5 m length were used for each plot. Inter-row and intra-row spacing of 90 cm and 20 cm, respectively, were used to make up plot sizes of 22.5 m²(5 rows x 5 m x 0.9 m) each. This translates to a population of about 55,000 plants on a per hectare basis.

Where,

r = number of replications;

g = number of genotypes;

MSr = mean square due to replications;

MSg = mean square due to genotypes;

MSe = mean square of error; and

σ²r, σ²g, and σ²e are variances due to replication, genotype, and error, respectively.

Analysis of variance in a randomized complete block design was computed using the following model:

Yij = µ + rj + gi + εij

Where,

Yij = the response of trait Y in the ith genotype and the jth replication

µ= the grand mean of trait Y;

rj = the effect of the jth replication;

gi = the effect of the ith genotype; and

εij = experimental error effect.

Phenotypic and genotypic variances

The phenotypic and genotypic variances of each trait were estimated from the RCBD analysis of variance and the expected mean squares under the assumption of random effects model computed from linear combinations of the mean squares and the phenotypic and genotypic coefficient of variations, which were also computed as per the methods suggested by Burton et al. (1953).

Genotypic variance (σ²g) =

Environmental variance (σ²e) = MSe

Where,

MSg and MSe are the mean sum of squares for the genotypes and error in the analysis of variance, respectively.

r is the number of replications.

Then, the phenotypic variance was estimated as the sum of the genotypic and environmental variances:

Phenotypic variance (σ²ph) = σ²g + σ²e

Genotypic and phenotypic coefficient of variations

The genotypic and phenotypic coefficients of variability were estimated according to the formulae of Singh and Chaudhary, (1977) as follows

Genotypic Coefficient of Variation (GCV) = (σg/grand mean)*100

Phenotypic Coefficient of Variation (PCV) = (σph/grand mean)*100

Where, σg and σph are genotypic and phenotypic standard deviations, respectively.

Heritability analysis

Broad sense heritability values were estimated based on the formula of Falconer et al., 1996 as follows:

Heritability in broad sense (H²) = (σ²g/σ²ph)*100

Then, the genetic advance for selection intensity (k) at 5% was estimated by the following formula (Allard, 1960):

EGA = k*σph*H²

Where,

EGA represents the expected genetic advance under selection;

σph is the phenotypic standard deviation;

H² is heritability in broad sense and k is selection intensity.

The genetic advance as percent of population mean was also estimated following the procedure of Johnson et al. (1955b):

Genetic advance as percent of population mean = (EGA/grand mean)*100

Correlation analysis

Estimations of genotypic and phenotypic correlation coefficients were done based on the procedure of Dabholkar (1992) as follows:

Genotypic correlation coefficient (rg) =

Phenotypic correlation coefficient (rph) =

Where,

COVg(xy) and COVph(xy) are the genotypic and phenotypic covariance of two variables (X and Y), respectively; σg(x) and σg(y) are the genotypic standard deviations for variables X and Y, respectively, while σph(x) and σph(y) are the phenotypic standard deviations of variables X and Y, respectively.

The calculated phenotypic correlation values were tested for its significance using t-test:

t = rph/SE(rph)

Where,

rph = Phenotypic correlation; SErph) = Standard error of phenotypic correlation obtained using the following formula (Sharma, 1998).

SE(rph) = √(1-r²ph)/(n-2)

Where,

n is the number of genotypes tested,

r²ph is phenotypic correlation coefficient.

The coefficients of correlations at genotypic levels were also tested for their significance by the formula described by Robertson (1959) as indicated below:

t = rgxy/SErgxy

The calculated ''t'' values were compared with the tabulated ''t'' value at (n-2) degree of freedom at 5% level of significance. Where, n is number of genotypes.

SErgxy = √ (1-r²gxy)/2H²x.H²y

Where, H²x = Heritability of trait x; and H²y = Heritability of trait y.

RESULTS AND DISCUSSION

The analysis of variance results for the fourteen traits studied are given in Tables 3. Highly significant (P<0.01) differences among genotypes were observed days to seedling emergence, days to initial squaring, days to 50% flowering, days to 65% boll opening, plant height, number of nodes to first fruiting branch, number of monopodial branches per plant, number of sympodial branches per plant, number of bolls per plant, seed cotton yield, lint yield and lint percentage; while only boll weight showed non-significant difference among the tested genotypes.

The study results clearly showed that the presence of considerable variations among genotypes for many of the traits measured. This indicated the presence of appreciable variations among genotypes for most of the characters and justifies carrying out further genetic analysis and identifying important traits for future breeding work relevant to Benishangul-Gumuze Regional State and other similar cotton producing areas.

Table 4. Minimum and Maximum values, mean and standard error of mean (SE) for the 14 traits of 14 cotton genotypes.

Traits	Min. value	Genotypes with Min. value	Max. value	Genotype with Max. value	Mean	SE	CV (%)
DSE	4.33	WARC-5, -9, & -12	6	WARC-4	5.07	0.31	10.64
DIS	39	WARC-12 & Deltapine 90	51.33	WARC-1	44.02	0.41	4.39
DIF	64	WARC-7	71.33	WARC-9	67.95	0.47	1.21
D50F	93.67	WARC-6	98.33	WARC-5	95.43	0.83	1.51
D65BO	155	WARC-5	170.33	Stam-59A	162.31	2.42	2.59
PHt	80.73	WARC-10	125	Stam-59A	96.42	4.2	7.56
NFSB	2.6	WARC-9	4.07	WARC-8	3.37	0.21	10.87
NMoB	4.2	WARC-9	7.03	WARC-4	22.16	0.41	13.36
NSyB	3.67	WARC-9	7.33	WARC-12	5.67	0.45	13.8
NBP	10.47	WARC-9	16.97	WARC-4	14.36	0.72	8.68
BWt	2.56	WARC-6	3.02	WARC-4	2.76	0.14	9.18
SCY	946	WARC-1	1478.5	WARC-4	1201.43	78.99	11.39
LY	358.91	WARC-1	585.4	WARC-8	478	31.78	11.52
L% (GOT)	37.33	Deltapine-90	42.17	Stam-59A	39.77	0.27	1.19

DSE=Days from planting to seedling emergence; DIS=Days from seedling emergence to initial squaring; DIF=Days to initial flowering; D50F=Days to 50% flowering; D65BO=Days to 65% boll opening; PHt=Plant height; NFSB=Number of nodes to first fruiting or sympodial branch; NMoB= Number of monopodial branches per plant; NSyB=Number of sympodial branches per plant; NBP=Number of bolls per plant; BWt=Boll weight in grams; LY=Lint yield in kg per ha; L%=Lint percentage or GOT (Ginning out turn); SCY=Seed cotton yield in kg per ha.

Phenotypic and genotypic coefficients of variation (PCV)

Higher phenotypic and genotypic variances were obtained from days to initial squaring, days to 65% boll opening, plant height, seed cotton yield and lint yield, indicating high influence of the environment on these traits.

High phenotypic coefficient of variation (PCV) values was noted on number of days to seedling emergence, plant height, number of nodes to the first fruiting branch, number of monopodial branch per plant, number of sympodial branch per plant, number of bolls per plant, seed cotton yield and lint yield. The PCV values for days to initial squaring and boll weight were medium (10-20%). Days to initial flowering, days to 50% flowering, days to 65% boll opening and lint percentage (GOT) had low values (< 10 %).

Estimation of broad-sense heritability

Estimates of heritability in broad sense ranged from 45% for average boll weight to 97% for lint percentage (GOT) (Table 6). Pramoda and Gangaprasad (2007) generally classified heritability estimates as low (< 40%), medium (40-59%), moderately high (60-79%) and very high (80-100%). If heritability were 100 %, which is genotypic variance (s²g) is equal to phenotypic variance (s²p), and then phenotypic performance would be a perfect indication of genotypic value (Johnson et al., 1955). Based on this bench mark, moderately high heritability (60-79%) was noted for days to seedling emergence, days to 50% flowering, days to 65% boll opening, number of nodes to first fruiting branch, number of monopodial branches per plant, seed cotton yield and lint yield. And genotypes which have high range of heritability (80-100%) were noted for days to initial squaring, days to initial flowering, plant height, number of sympodial branches per plant, number of bolls per plant and lint percentage. This result agrees with Amir et al., (2012) and Ali et al., (2011).

Moderately high heritability (60-79%) but low genetic advance as percent of mean was observed for the traits days to initial flowering, days to 50% flowering and days to 65% boll opening. For both testing sites, high heritability and yet low genetic advance as percent of mean indicated the involvement of non-additive gene actions for expression of the traits.

Generally, most of the traits studied showed moderately high to high heritability estimates indicating the possibility of improving these traits through selection. According to Poehlmon and David (1995) if a trait has high heritability accompanied with high genetic advance as percent of mean value, it indicates that the influence of the environment on the trait is less and selection for that trait becomes easy. The present study results are also in agreement with those obtained by Abbas et al., (2013), Dhivya et al., (2014) and Farooq et al., (2013).

Genetic advance as percent of mean

The genetic advance as the percentage of the mean (GAM) at 5% selection intensity is presented in Tables 3. It ranged from 4.68 for days to 50% flowering to 60.62 for sympodial branches per plant followed by monopodial branches per plant (44.98), lint yield (41.68), plant height (41.02), seed cotton yield (38.13) and number of bolls per plant (37.12).

Plant height and sympodial branches per plant showed moderately high heritability coupled with high genetic advance as percent of mean. These results indicate that there is good opportunity to improve these traits through crossing and selection.

In general, plant height, monopodial branch per plant, sympodial branch per plant, boll number per plant, seed cotton yield and lint yield showed moderately high heritability coupled with high genetic advance as percent of mean value.

Correlations of seed cotton yield and yield related traits

As shown in the Table 7, seed cotton yield had highly significant and positive phenotypic correlation with days to 50% flowering (rp = 0.61), days to 65% boll opening (rp = 0.69), plant height (rp = 0.89), number of sympodial branches per plant (rp = 0.87), number of bolls per plant (rp = 0.81) boll weight (rp =0.79), lint yield (rp=0.86) and lint percentage (0.83). Days to initial flowering, number of nodes to the first fruiting branch and number of monopodial branches per plant also showed significance and positive correlation with seed cotton yield. Days to initial squaring had positive association with seed cotton yield but not significant correlation.

At genotypic level, days to 50% flowering, plant height, number of sympodial branches per plant, number of bolls per plant, lint yield and lint percentage were observed to have positive and highly significant (p ≤ 0.01) correlations with seed cotton yield. At genotypic level seed cotton yield was significantly and positively correlated with days to initial flowering (rg = 0.51*), number of nodes to the first fruiting branch (rg = 0.63*) and Number of monopodial branch per plant (rg = 0.57*). The strong positive correlation of days to 50% flowering, plant height, sympodial branch per plant, number of bolls per plant, lint yield and lint percentage with seed cotton yield indicated that these characters might be utilized as selection criteria for improving seed cotton yield in upland cotton (G. hirustum. L.).

Table 5. Range, mean, SE, 𝝈𝟐𝒑, 𝝈𝟐𝒈, 𝝈𝟐𝒆, GCV, PCV, h2, GA and GAM% for the 14 characters of G. hirustum L. genotypes

Traits	Range	Mean	SE	σ² P	σ² G	σ² e	PCV%	GCV%	h²	GA	GAM%
DSE	4.33-6.00	5.07	0.31	1.33	1.04	0.29	22.78	20.14	0.78	1.86	36.73
DIS	39.00-51.33	44.14	0.41	37.37	33.60	3.77	13.85	13.13	0.90	11.34	25.69
DIF	64.00-71.33	67.95	0.47	14.13	13.46	0.67	5.53	5.40	0.95	7.39	10.87
D50F	94.00-98.00	95.43	0.83	8.34	6.26	2.08	3.03	2.62	0.75	4.47	4.68
D65BO	155.00-170.00	162.31	2.42	77.91	60.31	17.60	5.44	4.78	0.77	14.10	8.68
PHt	82.33-125.00	96.41	4.20	464.96	413.40	51.56	22.37	21.09	0.89	39.55	41.02
NFSB	2.60-4.07	3.37	0.21	0.56	0.43	0.13	22.21	19.37	0.76	1.17	34.85
NMoB	4.20-7.03	5.34	0.41	2.26	1.75	0.51	28.15	24.77	0.77	2.40	44.98
NSyB	3.67-7.30	5.66	0.45	3.88	3.28	0.61	34.85	32.00	0.84	3.43	60.62
NBP	10.47-16.97	14.36	0.72	9.54	7.98	1.56	21.50	19.67	0.84	5.33	37.12
BWt	2.53-3.02	2.76	0.14	0.11	0.05	0.06	12.02	8.10	0.45	0.31	11.27
SCY	946.00-1478.50	1201.43	78.99	82495.00	63775.60	18719.01	23.91	21.02	0.77	458.08	38.13
LY	358.31-584.91	478.00	31.78	14767.15	11736.65	3030.50	25.42	22.66	0.79	199.25	41.68
L% (GOT)	37.33-42.13	39.77	0.27	8.60	8.38	0.22	7.38	7.28	0.97	5.89	14.82

Table 6. Mean values of 14 traits of 14 cotton genotypes tested at Homosha in 2017.

Genotypes

DSE

DIS

DIF

D50F

D65BO

PHt

NFSB

NMoB

NSyB

NBP

BWt

SCY

WARC-1

5.67ba

51.33a

71.00ba

96.33bac

161.00edfc

84.13ge

3.47bac

6.30ba

5.27bdec

15.06bac

2.86bac

358.31e

37.867e

946.00f

WARC-2

5.00bdc

47.67b

67.00gfe

94.00dc

161.33edfc

100.00cbd

3.40bc

4.60ecd

5.00de

13.00dc

2.78bac

537.81ba

39.36d

1366.20ba

WARC-3

5.33bac

47.00bc

66.00g

96.67ba

169.00ba

103.73cbd

3.33bc

5.30bcd

5.73bdec

14.40bc

2.88bac

490.78bc

37.50e

1306.70bd

WARC-4

6.00a

47.00bc

66.67gf

94.00dc

159.67edf

91.73gefd

3.47bc

7.03a

6.47bac

16.97a

3.02a

583.47a

39.47d

1478.50a

WARC-5

4.33d

45.67d

68.67dc

98.00a

155.33f

89.07gef

3.67dc

4.40ed

7.20a

14.67bc

2.97ba

483.60dc

41.30bc

1172.70fc

WARC-6

4.67dc

41.00f

66.67gf

93.67d

162.30ebdfc

94.07cefd

3.80ba

5.20bcd

4.53fe

13.53dc

2.56bc

454.00bd

40.74c

1114.30fe

WARC-7

5.33bac

45.33ed

69.67bc

94.00dc

163.67edac

91.80gefd

3.30bc

6.27ba

6.47bac

16.07ba

2.89bac

584.91a

41.27bc

1418.30ba

WARC-8

5.66ba

44.33e

64.00h

97.33ba

158.00ef

82.33ge

4.067a

5.30bed

4.93fde

14.33bc

2.62bac

485.40dc

38.86d

1248.40bc

WARC-9

4.33d

41.00f

71.33a

97.00ba

159.67edf

103.73cbd

2.60d

4.20e

3.67f

10.47e

2.62bac

451.10bd

41.67ba

1083.20e

WARC-10

5.67ba

41.33f

67.67dfe

94.00dc

167.67bac

80.73g

2.87dc

4.60ecd

4.67fe

12.13ed

2.62bac

505.4bac

41.02bc

1231.90bd

WARC-11

4.67dc

46.00cd

70.67ba

95.30bc

161.33edfc

104.07cb

3.00dc

5.40bcd

6.20bdac

14.60bc

2.77bac

499.60ba

40.53c

1232.40bc

WARC-12

4.33d

39.00g

68.33dce

94.00dc

166.67bdac

108.467b

3.47bc

5.40bcd

7.33a

16.20ba

2.73bac

413.30dc

37.71e

1096.50fd

Deltpine 90

4.33d

39.00g

67.67dfe

95.00bdc

155.67f

91.00gef

3.87ba

5.73bc

5.20dec

14.80bc

2.71bac

394.08ed

37.33e

1057.80fe

Stam-59A

5.67ba

40.67f

66.00g

96.67ba

170.33a

125.00a

3.40bc

4.93ecd

6.53ba

14.87bc

2.57bc

450.00bdc

42.17a

1067.20fe

Mean

5.07

44.14

67.95

95.43

162.26

96.41

3.37

5.34

5.66

14.36

2.76

478

39.77

1201.43

LSD

0.91

3.26

1.38

2.42

7.08

12.23

0.61

1.2

1.31

2.09

0.43

92.39

0.79

229.63

CV%

10.64

4.39

1.21

1.51

2.59

7.56

10.87

13.36

13.8

8.68

9.18

11.51

1.19

11.39

In the same column, means followed by the same letter are not significantly different at the 5% level of significance.

DSE=Days from planting to seedling emergence; DIS=Days from seedling emergence to initial squaring; DIF=Days to initial flowering; D50F=Days to 50% flowering; D65BO=Days to 65% boll opening; PHt=Plant height; NFSB=Number of nodes to first fruiting or sympodial branch; NMoB= Number of monopodial l branches per plant; NSyB=Number of sympodial branches per plant; NBP=Number of bolls per plant; BWt=Boll weight in grams; LY=Lint yield in kg per ha; L%=Lint percentage or GOT (Ginning out turn); SCY=Seed cotton yield in kg per ha.

Table 7. Estimates of genotypic (above diagonal) and phenotypic (below diagonal) correlation coefficients for 14 traits of 14 cotton genotypes.

Traits

DSE

DIS

DIF

D50F

D65BO

PHt

NFSB

NMoB

NSyB

NBP

BWt

SCY

DSE

0.43*

0.65*

0.84**

0.08

0.27

0.53

0.23ns

0.06ns

0.08

0.01

0.21ns

-0.31ns

0.42ns

DIS

0.52**

0.89**

0.75*

0.53*

-0.57*

-0.48**

-0.35ns

-0.37*

0.45*

0.25ns

0.24ns

0.25ns

0.32ns

DIF

0.53**

0.55**

0.64*

0.51*

0.47

0.41*

0.57*

0.47

0.56*

0.38ns

0.19ns

0.41*

0.35*

D50F

0.45**

0.58*

0.65**

0.72*

0.74*

0.09

-0.83**

0.36*

0.63**

0.67**

0.26ns

0.35*

0.75**

D65BO

0.24ns

0.31ns

0.54*

0.51*

0.84*

0.43

-0.79*

0.33*

-0.42*

0.79**

- 0.52ns

0.31ns

0.59*

PHt

0.13ns

0.18ns

0.19ns

0.65*

0.59*

0.53

0.37**

-0.22

0.36**

0.25ns

0.66**

0.62*

0.83**

NFSB

0.09ns

0.23

0.25

0.55*

0.87**

-0.59*

0.38**

0.68*

0.52*

0.23ns

0.53*

0.45*

0.57*

NMoB

0.21ns

0.87*

-0.58*

-0.54*

-0.66*

0.68*

0.35**

0.59*

0.65*

0.54*

-0.38*

-0.66*

-0.69*

NSyB

0.33ns

0.26ns

0.53*

0.82**

0.73*

0.59*

0.98**

-57*

0.38*

0.57*

0.61*

0.59**

0.89**

NBP

0.24ns

0.29ns

0.08ns

0.25ns

0.79**

0.37**

0.86**

0.51*

0.93**

-0.59*

0.48**

0.67**

0.78**

BWt

0.35ns

0.07ns

0.56**

0.84**

0.69*

0.71*

0.55

0.88*

-0.66*

0.26

-0.23

0.48*

0.37ns

0.22ns

0.56*

0.55*

0.61*

0.78**

0.56*

-0.35*

0.66*

0.78**

0.37

0.94**

0.88**

0.18ns

-0.28

0.45*

0.49*

0.65*

0.71*

0.48*

0.47*

0.58**

0.79**

-0.19

0.79**

0.65**

SCY

- 0.33ns

0.38ns

0.51*

0.61**

0.69**

0.89**

0.63*

0.57*

0.87**

0.81**

0.79**

0.86**

0.83**

*, **Indicate significant at the 0.05 and 0.01 probability levels, respectively.

CONCLUSION

The analysis of variance showed significant differences among the tested genotypes for all characters considered in the study; this indicated the existence of variability among the tested genotypes. Phenotypic variances and phenotypic coefficients of variation were higher than their respective genotypic variances and genotypic coefficients of variation for all the traits considered in the study. This indicated the presence of environmental influence to some degree in the phenotypic expression of the traits.

Estimates of heritability in a broad sense ranged from 45% for boll weight to 95% for days to initial flowering. Moderately high heritability values were noted for days to seedling emergence, days to 50% flowering, days to 65% boll opening, number of nodes to the first fruiting branch, number of monopodial branches per plant, seed cotton yield and lint yield. Genotypes with high heritability values were noted for days to initial squaring, days to initial flowering, plant height, number of sympodial branches per plant, and number of bolls per plant, indicating that these traits are less affected by environmental conditions. High heritability but low genetic advance as percent of mean revealed the involvement of non-additive gene actions for the expression of the traits. The high heritability estimates suggested that the traits are primarily under genetic control and selection for them can be achieved through their phenotypic performance. Generally, most of the traits studied showed moderately high to high heritability estimates indicating the possibility of improving these traits through selection.

Correlation analysis among the characters studied revealed that positive and significant association of seed cotton yield and its components were more explained at phenotypic than at genotypic level. This implies that the correlation of these characters is reasonably expressed as a result of environmental factors rather than their genetic characteristics.

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ARTICLE INFO	ABSTRACT
*Article No.:* 122118179 *Type: Research* *DOI:* 10.15580/GJAS.2018.12.122118179	Seed cotton yield and fiber quality traits are controlled by many genes and also greatly affected by biotic and abiotic environmental factors. Hence, selection based on only yield would not be effective. In order to improve the yield potential of the cotton cultivars, an understanding of the relationship among various traits is of more importance. The current research was aimed to determine and record phenotypic and genotypic variation of elite cotton lines for utilization of the information in the breeding program to enhance cotton crop productivity and production in Ethiopia. Thus, 14 genotypes, five rows each, were evaluated in three replications at Homosha district of Benishangul-Gumz. The results depicted significant differences (P ≤0.05) among the varieties for all the studied traits, exhibiting the availability of substantial genetic variability among the cultivars for studied traits. Hence, these promising cultivars can further be exploited in various breeding programs to improve various characters of the cotton genotypes. Furthermore, correlation analysis showed that sympodial branches plant-1, boll weight and bolls plant-1 made significant and positive associations with seed cotton yield plant-1. Thus, selection for these traits will ultimately enhance the chances of increasing seed cotton yield plant-1. High heritability estimates were found for all studied traits with the exception of monopodial branches plant-1, indicating that these traits were inherited together and direct selection may be proved to be useful for these traits.
*Submitted:* 21/12/2018 *Accepted:* 27/12/2018 *Published:* 03/01/2019
Corresponding Author Kedir* Wulchafo Hussen *E-mail:* kedir.wulchafo@ gmail.com *Phone:* +251910716747
*Keywords:* Fiber traits; Yield traits; Gossypium hirsutum

Item number	Name of Genotypes	Pedigree/description	Selection number
1	WARC-1	HTO#052 x Deltapine 90	21-7
2	WARC-2	Cucurova1518 x LG-450	35-4
3	WARC-3	Deltapine 90 x Cucurova1518	37-7
4	WARC-4	Deltapine 90 x Stam-59A	38-8
5	WARC-5	Del Cero x GL-7	8-2
6	WARC-6	ISA 205H x Stam-59A	11-4
7	WARC-7	ISA 205H x Beyazealtin/5	16-2
8	WARC-8	HS-46 x Stoneville 453	19-2
9	WARC-9	HS-46 x Stoneville 453	19-8
10	WARC-10	Stam-59 A x Cucurova 1518	30-2
11	WARC-11	Stam-59 A x Cucurova 1518	30-6
12	WARC-12	Stam-59A x Europa-5	-
13	Deltapine 90 (Check)	n.a.; Introduced from the USA in the 1980’s	-
14	Stam-59A (Check)	n.a.; Imported from Mali in 2004 through technology shopping.	-

Source of Variation	Df	Mean Square	Expected Mean Square
Replication	r-1	MSr	σ²e + gσ²r
Genotypes	g-1	MSg	σ²e + rσ²g
Error	(r-1) (g-1)	MSe	σ²e

Traits	Replication	Genotype	Error	CV (%)
DSE	0.21ns	1.13**	0.29	10.64
DIS	8.00ns	34.86**	3.77	4.39
DIF	0.17*	13.68**	0.68	1.21
D50F	0.93ns	6.95**	2.08	1.51
D65BO	39.59	66.36**	17.77	2.59
PHt	170.99*	431078.00**	53.09	7.56
NFSB	1.98**	0.47**	0.13	10.87
NMoB	3.32**	1.92**	0.51	13.36
NSyB	9.93**	3.48**	0.61	13.80
NBP	31.28**	8.50**	1.56	8.68
BWt	0.11ns	0.07ns	0.06	9.18
SCY	13004.87ns	70015.24**	18719.01	11.39
LY	1212.19ns	12746.82**	3030.50	11.52
L% (GOT)	0.94*	8.45**	0.22	1.19